Lang Segment Anything – Object Detection and Segmentation With Text Prompt

Meta AI’s Segment Anything Model (SAM) is a prompt-driven computer vision system that produces object masks using an image encoder, a prompt encoder, and a mask decoder, supporting inputs such as points, boxes, rough masks, and, conceptually, text. Its Vision Transformer-based architecture allows images to be encoded once and then segmented efficiently through lightweight prompting, while its adaptable pre-trained design can be fine-tuned for specialized applications. Although SAM’s text-enabled model weights have not been released, text-guided segmentation can be achieved through CLIP-aligned embeddings or through Luca Medeiros’s lang-segment-anything library, which combines GroundingDINO’s zero-shot text-to-bounding-box detection with SAM’s mask generation. The project also demonstrates deployment through a Lightning AI and Gradio application, allowing users to choose a SAM model, set detection thresholds, upload an image, and provide a text prompt to visualize detected objects and segmentation masks.

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